Advanced Lectures on Machine Learning

Olivier Bousquet

ISBN 10: 3540231226 / ISBN 13: 9783540231226

Published by Springer-Verlag Berlin and Heidelberg GmbH and Co. KG, DE, 2004

About this Item

Machine Learning has become a key enabling technology for many engineering applications, investigating scientific questions and theoretical problems alike. To stimulate discussions and to disseminate new results, a summer school series was started in February 2002, the documentation of which is published as LNAI 2600.This book presents revised lectures of two subsequent summer schools held in 2003 in Canberra, Australia, and in Tübingen, Germany. The tutorial lectures included are devoted to statistical learning theory, unsupervised learning, Bayesian inference, and applications in pattern recognition; they provide in-depth overviews of exciting new developments and contain a large number of references.Graduate students, lecturers, researchers and professionals alike will find this book a useful resource in learning and teaching machine learning.

Seller Inventory # LU-9783540231226

Bibliographic Details

• Title: Advanced Lectures on Machine Learning
• Publisher: Springer-Verlag Berlin and Heidelberg GmbH and Co. KG, DE
• Publication Date: 2004
• Language: English
• ISBN 10: 3540231226
• ISBN 13: 9783540231226
• Binding: Paperback
• Edition: 2004 ed.
• Condition: New
• Dimensions: 15.49W x 1.47H x 23.5D centimeters 15.49 centimeters width by 1.47 centimeters height by 23.5 centimeters depth
• Weight: 820 grams

About this title

Synopsis

Machine Learning has become a key enabling technology for many engineering applications, investigating scientific questions and theoretical problems alike. To stimulate discussions and to disseminate new results, a summer school series was started in February 2002, the documentation of which is published as LNAI 2600.

This book presents revised lectures of two subsequent summer schools held in 2003 in Canberra, Australia, and in Tübingen, Germany. The tutorial lectures included are devoted to statistical learning theory, unsupervised learning, Bayesian inference, and applications in pattern recognition; they provide in-depth overviews of exciting new developments and contain a large number of references.

Graduate students, lecturers, researchers and professionals alike will find this book a useful resource in learning and teaching machine learning.